R Tutorial

An introduction to R

Introduction

This tutorial is will introduce the reader to , a free, open-source statistical computing environment often used with RStudio, a integrated development environment for .

R Project Logo

Download

Download at https://www.r-project.org/

Download RStudio at https://rstudio.com/products/rstudio/download/

Calculator

can be used as a super awesome calculator

# 5 + 3 = 8
5 + 3

## [1] 8

# 24 / (1 + 2) = 8
24 / (1 + 2)

## [1] 8

# 2 * 2 * 2 = 8
2^3

## [1] 8

# 8 * 8 = 64
sqrt(64)

## [1] 8

# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000)

## [1] 8

Functions

has many useful built in functions

1:10

##  [1]  1  2  3  4  5  6  7  8  9 10

as.character(1:10)

##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

rep(1:2, times = 5)

##  [1] 1 2 1 2 1 2 1 2 1 2

rep(1:5, times = 2)

##  [1] 1 2 3 4 5 1 2 3 4 5

rep(1:5, each = 2)

##  [1] 1 1 2 2 3 3 4 4 5 5

rep(1:5, length.out = 7)

## [1] 1 2 3 4 5 1 2

seq(5, 50, by = 5)

##  [1]  5 10 15 20 25 30 35 40 45 50

seq(5, 50, length.out = 5)

## [1]  5.00 16.25 27.50 38.75 50.00

paste(1:10, 20:30, sep = "-")

##  [1] "1-20"  "2-21"  "3-22"  "4-23"  "5-24"  "6-25"  "7-26"  "8-27"  "9-28"  "10-29" "1-30"

paste(1:10, collapse = "-")

## [1] "1-2-3-4-5-6-7-8-9-10"

paste0("x", 1:10)

##  [1] "x1"  "x2"  "x3"  "x4"  "x5"  "x6"  "x7"  "x8"  "x9"  "x10"

min(1:10)

## [1] 1

max(1:10)

## [1] 10

range(1:10)

## [1]  1 10

mean(1:10)

## [1] 5.5

sd(1:10)

## [1] 3.02765

Custom Functions

Users can also create their own functions

customFunction1 <- function(x, y) {
  z <- 100 * x / (x + y)
  paste(z, "%")
}
customFunction1(x = 10, y = 90)

## [1] "10 %"

customFunction2 <- function(x) {
  mymin <- mean(x - sd(x))
  mymax <- mean(x) + sd(x)
  print(paste("Min =", mymin))
  print(paste("Max =", mymax))
}
customFunction2(x = 1:10)

## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"

`for` loops and `if` `else` statements

xx <- NULL #creates and empty object
for(i in 1:10) {
  xx[i] <- i*3
}
xx

##  [1]  3  6  9 12 15 18 21 24 27 30

xx %% 2 #gives the remainder when divided by 2

##  [1] 1 0 1 0 1 0 1 0 1 0

for(i in 1:length(xx)) {
  if((xx[i] %% 2) == 0) {
    print(paste(xx[i],"is Even"))
  } else { 
      print(paste(xx[i],"is Odd")) 
    }
}

## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"

# or
ifelse(xx %% 2 == 0, "Even", "Odd")

##  [1] "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even"

paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))

##  [1] "3 is Odd"   "6 is Even"  "9 is Odd"   "12 is Even" "15 is Odd"  "18 is Even" "21 is Odd"  "24 is Even" "27 is Odd"  "30 is Even"

Objects

Information can be stored in user defined objects, in multiple forms:

c(): a string of values
matrix(): a two dimensional matrix in one format
data.frame(): a two dimensional matrix where each column can be a different format
list():

A string…

xc <- 1:10
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

xc <- c(1,2,3,4,5,6,7,8,9,10)
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

A matrix…

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1    2    3    4    5    6    7    8    9    10
##  [2,]   11   12   13   14   15   16   17   18   19    20
##  [3,]   21   22   23   24   25   26   27   28   29    30
##  [4,]   31   32   33   34   35   36   37   38   39    40
##  [5,]   41   42   43   44   45   46   47   48   49    50
##  [6,]   51   52   53   54   55   56   57   58   59    60
##  [7,]   61   62   63   64   65   66   67   68   69    70
##  [8,]   71   72   73   74   75   76   77   78   79    80
##  [9,]   81   82   83   84   85   86   87   88   89    90
## [10,]   91   92   93   94   95   96   97   98   99   100

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

A data frame…

xd <- data.frame(
  x1 = c("aa","bb","cc","dd","ee",
         "ff","gg","hh","ii","jj"),
  x2 = 1:10,
  x3 = c(1,1,1,1,1,2,2,2,3,3),
  x4 = rep(c(1,2), times = 5),
  x5 = rep(1:5, times = 2),
  x6 = rep(1:5, each = 2),
  x7 = seq(5, 50, by = 5),
  x8 = log10(1:10),
  x9 = (1:10)^3,
  x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

A list…

xl <- list(xc, xm, xd)
xl[[1]]

##  [1]  1  2  3  4  5  6  7  8  9 10

xl[[2]]

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

xl[[3]]

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

Selecting Data

xc[5] # 5th element in xc

## [1] 5

xd$x3[5] # 5th element in col "x3"

## [1] 1

xd[5,"x3"] # row 5, col "x3"

## [1] 1

xd$x3 # all of col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[,"x3"] # all rows, col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[3,] # row 3, all cols

##   x1 x2 x3 x4 x5 x6 x7        x8 x9  x10
## 3 cc  3  1  1  3  2 15 0.4771213 27 TRUE

xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"

##   x4 x5
## 2  2  2
## 4  2  4

xl[[3]]$x1 # 3rd object in the list, col "x1

##  [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"

regexpr

xx <- data.frame(Name = c("Item 1 (detail 1)",
                          "Item 20 (detail 20)",
                          "Item 300 (detail 300)"),
                 Item = NA,
                 Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx

##                    Name     Item     Detail
## 1     Item 1 (detail 1)   Item 1   detail 1
## 2   Item 20 (detail 20)  Item 20  detail 20
## 3 Item 300 (detail 300) Item 300 detail 300

Data Formats

Data can also be saved in many formats:

numeric
integer
character
factor
logical

xd$x3 <- as.character(xd$x3)
xd$x3

##  [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"

xd$x3 <- as.numeric(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3

xd$x3 <- as.factor(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3

xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1

xd$x10

##  [1]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE

as.numeric(xd$x10) # TRUE = 1, FALSE = 0

##  [1] 1 1 1 0 0 1 1 0 0 0

sum(xd$x10)

## [1] 5

Internal structure of an object can be checked with str()

str(xc) # c()

##  num [1:10] 1 2 3 4 5 6 7 8 9 10

str(xm) # matrix()

##  int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...

str(xd) # data.frame()

## 'data.frame':    10 obs. of  10 variables:
##  $ x1 : chr  "aa" "bb" "cc" "dd" ...
##  $ x2 : int  1 2 3 4 5 6 7 8 9 10
##  $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
##  $ x4 : num  1 2 1 2 1 2 1 2 1 2
##  $ x5 : int  1 2 3 4 5 1 2 3 4 5
##  $ x6 : int  1 1 2 2 3 3 4 4 5 5
##  $ x7 : num  5 10 15 20 25 30 35 40 45 50
##  $ x8 : num  0 0.301 0.477 0.602 0.699 ...
##  $ x9 : num  1 8 27 64 125 216 343 512 729 1000
##  $ x10: logi  TRUE TRUE TRUE FALSE FALSE TRUE ...

str(xl) # list()

## List of 3
##  $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
##  $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
##  $ :'data.frame':    10 obs. of  10 variables:
##   ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
##   ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
##   ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
##   ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
##   ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
##   ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
##   ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
##   ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
##   ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
##   ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...

Packages

Additional libraries can be installed and loaded for use.

install.packages("scales")

library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx

##    Values  Rescaled
## 1       1  1.000000
## 2       2  4.222222
## 3       3  7.444444
## 4       4 10.666667
## 5       5 13.888889
## 6       6 17.111111
## 7       7 20.333333
## 8       8 23.555556
## 9       9 26.777778
## 10     10 30.000000

libraries can also be used without having to load them

scales::rescale(1:10, to = c(1,30))

##  [1]  1.000000  4.222222  7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000

Data Wrangling

R for Data Science - https://r4ds.had.co.nz/

xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
                 Data1 = 1:10, 
                 Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      X     6    60       66     6000
## 7      X     7    70       77     7000
## 8      X     8    80       88     8000
## 9      Y     9    90       99     9000
## 10     Y    10   100      110    10000

xx$Data1 < 5 # which are less than 5

##  [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE

xx[xx$Data1 < 5,]

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx[xx$Group == "X", c("Group","Data2","NewData1")]

##   Group Data2 NewData1
## 1     X    10       11
## 2     X    20       22
## 6     X    60       66
## 7     X    70       77
## 8     X    80       88

Data wrangling with tidyverse and pipes (%>%)

library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
  mutate(Data1 = 1:10, 
         Data2 = seq(10, 100, by = 10),
         NewData1 = Data1 + Data2,
         NewData2 = Data1 * 1000)
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      Y     6    60       66     6000
## 7      Y     7    70       77     7000
## 8      X     8    80       88     8000
## 9      X     9    90       99     9000
## 10     X    10   100      110    10000

filter(xx, Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Group == "X") %>% 
  select(Group, NewColName=Data2, NewData1)

##   Group NewColName NewData1
## 1     X         10       11
## 2     X         20       22
## 3     X         80       88
## 4     X         90       99
## 5     X        100      110

xs <- xx %>% 
  group_by(Group) %>% 
  summarise(Data2_mean = mean(Data2),
            Data2_sd = sd(Data2),
            NewData2_mean = mean(NewData2),
            NewData2_sd = sd(NewData2))
xs

## # A tibble: 2 × 5
##   Group Data2_mean Data2_sd NewData2_mean NewData2_sd
##   <chr>      <dbl>    <dbl>         <dbl>       <dbl>
## 1 X             60     41.8          6000       4183.
## 2 Y             50     15.8          5000       1581.

xx %>% left_join(xs, by = "Group")

##    Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1      X     1    10       11     1000         60 41.83300          6000    4183.300
## 2      X     2    20       22     2000         60 41.83300          6000    4183.300
## 3      Y     3    30       33     3000         50 15.81139          5000    1581.139
## 4      Y     4    40       44     4000         50 15.81139          5000    1581.139
## 5      Y     5    50       55     5000         50 15.81139          5000    1581.139
## 6      Y     6    60       66     6000         50 15.81139          5000    1581.139
## 7      Y     7    70       77     7000         50 15.81139          5000    1581.139
## 8      X     8    80       88     8000         60 41.83300          6000    4183.300
## 9      X     9    90       99     9000         60 41.83300          6000    4183.300
## 10     X    10   100      110    10000         60 41.83300          6000    4183.300

Read/Write data

xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)

For excel sheets, the package readxl can be used to read in sheets of data.

library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")

Tidy Data

Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html

Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html

yy <- xx %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = round(mean(DTF),1)) %>% 
  arrange(Location)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          Location            Mean_DTF
##   <chr>         <chr>                  <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh     86.7
## 2 ILL 618 AGL   Jessore, Bangladesh     79.3
## 3 Laird AGL     Jessore, Bangladesh     76.8
## 4 CDC Maxim AGL Metaponto, Italy       134. 
## 5 ILL 618 AGL   Metaponto, Italy       138. 
## 6 Laird AGL     Metaponto, Italy       137. 
## 7 CDC Maxim AGL Saskatoon, Canada       52.5
## 8 ILL 618 AGL   Saskatoon, Canada       47  
## 9 Laird AGL     Saskatoon, Canada       56.8

yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
##   <chr>                         <dbl>              <dbl>               <dbl>
## 1 CDC Maxim AGL                  86.7               134.                52.5
## 2 ILL 618 AGL                    79.3               138.                47  
## 3 Laird AGL                      76.8               137.                56.8

yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          TraitName           Value
##   <chr>         <chr>               <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh  86.7
## 2 ILL 618 AGL   Jessore, Bangladesh  79.3
## 3 Laird AGL     Jessore, Bangladesh  76.8
## 4 CDC Maxim AGL Metaponto, Italy    134. 
## 5 ILL 618 AGL   Metaponto, Italy    138. 
## 6 Laird AGL     Metaponto, Italy    137. 
## 7 CDC Maxim AGL Saskatoon, Canada    52.5
## 8 ILL 618 AGL   Saskatoon, Canada    47  
## 9 Laird AGL     Saskatoon, Canada    56.8

yy <- yy %>% spread(key = Name, value = Value)
yy

## # A tibble: 3 × 4
##   TraitName           `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
##   <chr>                         <dbl>         <dbl>       <dbl>
## 1 Jessore, Bangladesh            86.7          79.3        76.8
## 2 Metaponto, Italy              134.          138.        137. 
## 3 Saskatoon, Canada              52.5          47          56.8

Base Plotting

We will start with some basic plotting using the base function plot()

Tutorial 1 - http://www.sthda.com/english/wiki/r-base-graphs

Tutorial 2 - https://bookdown.org/rdpeng/exdata/the-base-plotting-system-1.html

# A basic scatter plot
plot(x = xd$x8, y = xd$x9)

# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)

plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)

# Adjust plot type 
plot(x = xd$x8, y = xd$x9, type = "line")

# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)

# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")

Now lets create some random and normally distributed data to make some more complicated plots

# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru

##   [1] 13.5022028 45.1530008 72.3265441 35.6430728 70.3455447 86.5229260 17.4796292 52.7865581  9.4007879 52.5557988 48.2800907 15.3669801 66.7341581 51.0089363 65.0362645
##  [16] 92.9714086 87.1512151 55.8883105 82.0328587 92.9527914 59.9614501 55.0583679 86.7609692 81.4351032 66.2505263 99.6351311 66.8853928 55.1258925 72.1675699 29.5222487
##  [31] 25.0660759  4.8952654 12.5745349 46.6907365 17.6164689 65.6510925 51.0130739 35.0186071 68.8546889 91.7321545 66.6734419 43.3288923 77.9306557 95.5720124 70.9582566
##  [46] 83.0424098 74.0442151 28.8257818 90.6973983 88.5477356 39.1539735 25.3253396 50.6814125 42.7163268 13.0106627 44.2992976 31.7012511 43.2393122 34.1532303 32.3944021
##  [61] 92.9369486 24.0677104 13.6756950 70.6622269  0.1834377 10.5271406 38.4894103 98.6718460  2.0284608 54.4954011 19.3754994 33.0554224 64.7411025 16.2221864 92.3725151
##  [76]  9.1356384  0.7853962 49.3130363 54.9295055 71.5439691 17.5510948 88.9182463 72.9321697 73.5589645  8.6557539 69.8744946 81.1260354 61.0342902  6.8447232 58.9981868
##  [91] 50.5259881 11.9563868 89.4042926 33.5508144 33.5255252 79.3006716 93.4057693 68.8343693 71.6659850 62.7810296

plot(x = ru)

order(ru)

##   [1]  65  77  69  32  89  85  76   9  66  92  33  55   1  63  12  74   7  81  35  71  62  31  52  48  30  57  60  72  95  94  59  38   4  67  51  54  58  42  56   2  34
##  [42]  11  78  91  53  14  37  10   8  70  79  22  28  18  90  21  88 100  73  15  36  25  41  13  27  98  39  86   5  64  45  80  99  29   3  83  84  47  43  96  87  24
##  [83]  19  46   6  23  17  50  82  93  49  40  75  61  20  16  97  44  68  26

ru<- ru[order(ru)]
ru

##   [1]  0.1834377  0.7853962  2.0284608  4.8952654  6.8447232  8.6557539  9.1356384  9.4007879 10.5271406 11.9563868 12.5745349 13.0106627 13.5022028 13.6756950 15.3669801
##  [16] 16.2221864 17.4796292 17.5510948 17.6164689 19.3754994 24.0677104 25.0660759 25.3253396 28.8257818 29.5222487 31.7012511 32.3944021 33.0554224 33.5255252 33.5508144
##  [31] 34.1532303 35.0186071 35.6430728 38.4894103 39.1539735 42.7163268 43.2393122 43.3288923 44.2992976 45.1530008 46.6907365 48.2800907 49.3130363 50.5259881 50.6814125
##  [46] 51.0089363 51.0130739 52.5557988 52.7865581 54.4954011 54.9295055 55.0583679 55.1258925 55.8883105 58.9981868 59.9614501 61.0342902 62.7810296 64.7411025 65.0362645
##  [61] 65.6510925 66.2505263 66.6734419 66.7341581 66.8853928 68.8343693 68.8546889 69.8744946 70.3455447 70.6622269 70.9582566 71.5439691 71.6659850 72.1675699 72.3265441
##  [76] 72.9321697 73.5589645 74.0442151 77.9306557 79.3006716 81.1260354 81.4351032 82.0328587 83.0424098 86.5229260 86.7609692 87.1512151 88.5477356 88.9182463 89.4042926
##  [91] 90.6973983 91.7321545 92.3725151 92.9369486 92.9527914 92.9714086 93.4057693 95.5720124 98.6718460 99.6351311

plot(x = ru)

# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd

##   [1] 46.27076 58.10186 74.08223 47.30162 50.85514 35.10888 49.63683 70.63513 55.06543 48.91586 26.30750 52.28130 50.81744 63.93883 59.15893 63.23410 48.04466 52.11498
##  [19] 46.77409 54.79352 40.84279 41.42331 40.00042 61.55285 42.01685 63.10758 36.70894 54.21317 49.28433 46.92705 35.97228 57.86332 32.07007 60.90934 44.34071 55.18888
##  [37] 73.61276 51.00027 53.22685 47.43540 51.37452 64.82481 42.01904 48.84573 41.39814 55.76970 48.70247 35.18671 43.04197 51.43656 56.72635 33.98564 37.43570 51.93575
##  [55] 36.50384 36.54938 48.23625 58.96656 46.00078 49.16290 50.84516 49.77257 47.27189 51.83579 34.07118 57.92835 41.07410 40.59964 62.56938 59.90535 37.25794 42.31965
##  [73] 54.21652 51.81787 44.33395 63.87864 53.17549 56.50739 51.45644 60.08258 48.30303 41.34587 45.24126 51.95634 67.13339 51.35032 43.68141 52.16486 44.30335 66.05399
##  [91] 58.16523 67.13941 37.07933 47.12999 58.81637 58.40154 51.68896 56.38371 58.29836 60.77352

nd <- nd[order(nd)]
nd

##   [1] 26.30750 32.07007 33.98564 34.07118 35.10888 35.18671 35.97228 36.50384 36.54938 36.70894 37.07933 37.25794 37.43570 40.00042 40.59964 40.84279 41.07410 41.34587
##  [19] 41.39814 41.42331 42.01685 42.01904 42.31965 43.04197 43.68141 44.30335 44.33395 44.34071 45.24126 46.00078 46.27076 46.77409 46.92705 47.12999 47.27189 47.30162
##  [37] 47.43540 48.04466 48.23625 48.30303 48.70247 48.84573 48.91586 49.16290 49.28433 49.63683 49.77257 50.81744 50.84516 50.85514 51.00027 51.35032 51.37452 51.43656
##  [55] 51.45644 51.68896 51.81787 51.83579 51.93575 51.95634 52.11498 52.16486 52.28130 53.17549 53.22685 54.21317 54.21652 54.79352 55.06543 55.18888 55.76970 56.38371
##  [73] 56.50739 56.72635 57.86332 57.92835 58.10186 58.16523 58.29836 58.40154 58.81637 58.96656 59.15893 59.90535 60.08258 60.77352 60.90934 61.55285 62.56938 63.10758
##  [91] 63.23410 63.87864 63.93883 64.82481 66.05399 67.13339 67.13941 70.63513 73.61276 74.08223

plot(x = nd)

hist(x = nd)

hist(nd, breaks = 20, col = "darkgreen")

plot(x = density(nd))

boxplot(x = nd)

boxplot(x = nd, horizontal = T)

ggplot2

Lets be honest, the base plots are ugly! The ggplot2 package gives the user to create a better, more visually appealing plots. Additional packages such as ggbeeswarm and ggrepel also contain useful functions to add to the functionality of ggplot2.

ggplot2 - https://ggplot2.tidyverse.org/

Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html

Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/

The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html

library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()

mp + geom_point(aes(color = x3, shape = x3), size = 4)

mp + geom_line(size = 2)

mp + geom_line(aes(color = x3), size = 2)

mp + geom_smooth(method = "loess")

mp + geom_smooth(method = "lm")

xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
                          rnorm(50, mean = 60, sd = 5)),
                 group = factor(rep(1:2, each = 50)),
                 label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")

mp + geom_histogram(color = "black", position = "dodge")

mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1

mp + geom_density(alpha = 0.5)

mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")

mp + geom_boxplot() + geom_point()

mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")

library(ggbeeswarm)
mp + geom_quasirandom()

mp + geom_quasirandom(aes(shape = group))

mp2 <- mp + geom_violin() + 
  geom_boxplot(width = 0.1, fill = "white") +
  geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)

library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
          common.legend = T, legend = "bottom")

Statistics

Handbook of Biological Statistics - http://biostathandbook.com/

R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html

# Prep data
lev_Loc  <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
  mutate(Location = factor(Location, levels = lev_Loc),
         Name = factor(Name, levels = lev_Name))
xx <- dd %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
##   <fct>                       <dbl>                 <dbl>              <dbl>
## 1 ILL 618 AGL                  47                    79.3               138.
## 2 CDC Maxim AGL                52.5                  86.7               134.
## 3 Laird AGL                    56.8                  76.8               137.

# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
  geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF, 
                      color = Name, group = Name, shape = Name)) +
  geom_point(size = 2.5, alpha = 0.7) + 
  geom_line(size = 1, alpha = 0.7) +
  theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")

From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.

summary(aov(DTF ~ Name * Location, data = dd))

##               Df Sum Sq Mean Sq  F value   Pr(>F)    
## Name           2     88      44    3.476   0.0395 *  
## Location       2  65863   32931 2598.336  < 2e-16 ***
## Name:Location  4    560     140   11.044 2.52e-06 ***
## Residuals     45    570      13                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.

If we only have two things to compare, we could do a t-test.

xx <- dd %>% 
  filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
  spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -32.18265 -25.48402
## sample estimates:
## mean of x mean of y 
##  52.11111  80.94444

DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.

xx <- dd %>% 
  filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
         Location == "Metaponto, Italy") %>%
  spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.059739  7.059739
## sample estimates:
## mean of x mean of y 
##  137.8333  136.8333

DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.

pch Plot

xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
                 y = rep(5:1, each = 6, length.out = 26),
                 pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
  geom_point(color = "darkred", fill = "darkblue", size = 5) +
  geom_text(aes(label = pch), nudge_x = -0.25) +
  scale_shape_manual(values = xx$pch) +
  scale_x_continuous(breaks = 6:1) +
  scale_y_continuous(breaks = 6:1) +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5),
        axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(title = "Plot symbols in R (pch)",
       subtitle = "color = \"darkred\", fill = \"darkblue\"",
       x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")

R Markdown

Tutorials on how to create an R markdown document like this one can be found here:

https://rmarkdown.rstudio.com/articles_intro.html

https://rmarkdown.rstudio.com/lesson-1.html

https://alexd106.github.io/intro2R/Rmarkdown_intro.html

R Tutorial

An introduction to R

R Tutorial

Introduction

Download

Other Useful Tutorials

Calculator

Functions

Custom Functions

for loops and if else statements

Objects

A string…

A matrix…

A data frame…

A list…

Selecting Data

regexpr

Data Formats

Packages

Data Wrangling

Read/Write data

Tidy Data

Base Plotting

ggplot2

Statistics

pch Plot

R Markdown

`for` loops and `if` `else` statements